Artificial Intelligence: Cognitive Science

Multidisciplinary study of mind and cognition

Immerse yourself in the multidisciplinary study of mind and cognition. Researchers in Cognitive Science come from a wide range of backgrounds, including psychology, computer science, artificial intelligence, philosophy, mathematics and neuroscience. They all share the common goal of gaining a deeper understanding of the human mind, for both theoretical and practical purposes.

The track focusses on the processes that underlie human functioning from two different research perspectives: empirical work and computational modeling. The combination of these two perspectives allows for a better understanding of the mechanisms underlying human functioning. For example, empirical work may suggest a functional layout for computation models, and vice versa, results of simulations with computation models can provide suggestions for setting up specific experiments. The underlying philosophy of Cognitive Science at VU Amsterdam is to challenge students to be knowledgeable in a wide variety of fields and techniques, all of which are related to the subject area of cognitive psychology.

The Cognitive Science track is jointly organized by the Department of Cognitive Psychology of the Faculty of Psychology and Education, and the Department of Artificial Intelligence of the Faculty of Sciences.

Agent systems
A key goal of artificial intelligence is to develop agent systems that can make decisions and complete tasks without direct human supervision. Agent systems focuses on completing the perception-action loop: given the results of such perception, how should an agent act in order to reach its goal, maximise its utility, and minimise its costs? Autonomous robots are a prototypical example of such systems, though an agent system can also be computer that plays board games like chess and go, or a search engine that meets information needs and offers recommendations.

An interesting setting is that of multiple agents that collaborate and communicate to behave intelligently. This involves understanding each others goals and perceptions, and planning actions collaboratively.

Computational Intelligence
In Computational Intelligence (CI), we research techniques that achieve intelligence, or at least intelligent behaviour, by considering the behaviour that emerges from the interaction between relatively simple components in large collectives. The algorithms are often bio-inspired: they imitate aspects of behaviour found in nature. Examples are algorithms that mimic trail formation in ant colonies for optimal path planning. Evolutionary algorithms that mimic natural evolution to optimise robot control, or neural networks for predicting the course of a disease. CI employs these algorithms to develop systems that are adaptive, collective, autonomous and self-organising. In this profile, we particularly consider CI for systems like (future versions of) swarm robotic systems, smart environments, eHealth systems with interactive sensing devices and smart vehicles.

Because of the inherent complexity of CI systems and the difficulties in analytically predicting the behaviour that emerges, this profile has a strong experimental flavour: for CI algorithms, “the proof of the pudding is the eating”. As a student in Computational Intelligence you can gain real-life experience with applying and/or researching CI techniques. This can be through an internship at a company that exploits CI techniques, or you can become involved in one of our research projects in Evolutionary Robotics, Artificial Life and adaptive health systems. In the latter case, you will conduct proper scientific research aiming at a publication, typically at a prestigious conference.

Computer vision
Computer vision focuses on techniques and models for acquiring and analyzing images in order to understand objects and scenes in the real world. Computer vision is important for the construction of intelligent methods and techniques for (autonomous) systems that interpret sensory information and use that information to generate intelligent and goal-directed behavior.

Information Retrieval
The way we access, provide, and exchange information has changed dramatically with the rise of the Internet. Information retrieval studies and invents methods and techniques for the design, implementation, and use of information processing technology in the context of a variety of Internet applications, ranging from search engines to text analysis.

Information Retrieval has developed from a number of research areas, including Computer Science, Library Science, Artificial Intelligence, Data Mining, and Natural Language Processing. While Information Retrieval builds on techniques from a variety of research areas, there are a number of research problems that are specific to the Web applications, such as the design of Internet search engines, efficient linking of related information across the Web, improving information extraction from social networking sites, and the access of foreign language information. In addition, the sheer scale of the Internet opens up tremendous opportunities for data mining approaches, while at the same time posing interesting research challenges with respect to robustness and scalability.

Within the Information Retrieval profile you will be familiarized with several data mining, natural language processing, and link-based techniques that are not only relevant to this profile but also to many other Artificial Intelligence applications. It covers the well-established techniques within the area but is also looks forward, discussing the science behind cutting-edge technologies and anticipating Web technologies that yet have to be fully realized.

Knowledge representation
When humans reason about the world, we identify objects, we make categories of such objects, and we reason about the relations between the things in the world around us. How can we represent such knowledge in a computer, in such a way that a computer could reason about the world around it in a similar way? The field of Knowledge Representation and Reasoning aims to represent knowledge in such a form that a computer system can use it to solve complex tasks such as diagnosing a medical condition or having an intelligent dialog in a natural language. Knowledge representat­­ion and reasoning uses logic as its main mathematical tool, and tries to answer such questions as: how can we design logics that can efficiently reason with very large amounts of knowledge? Which logics are suited for reasoning about space and time? How can we deal with uncertainty and vagueness? How to reason about changes in the world around us? Knowledge Representation techniques are used in many practical applications. Examples are expert systems for medical diagnosis, decision support systems for judges, and intelligent dialogue systems such as Siri on the iPhone.­
Machine Learning
In Machine Learning we develop algorithms that can improve their performance by learning from experience. Experience often comes in the form of very large amounts of data, or "Big Data". The resulting algorithms and models are used for making predictions and for improving decisions. It has become a core technology for a wide variety of applications such as: text and image classification; information retrieval, robot control; discovering causal explanations, social network analysis, customer intelligence; anomaly detection, recommender systems, fraud detection, forecasting and so on.

Due to the increased availability of data from sensors (Internet-of-Things), the range of applications is growing fast. The emphasis in this profile is on algorithms and statistical models that explain why and when algorithms work. We also discuss a number of algorithms in detail, such as clustering, dimensionality reduction, regression and classification, graphical models and deep learning. The profile has a strong mathematical component, but there is also an emphasis on developing the skills to implement machine learning algorithms through project assignments. As a student in Machine Learning you can do your master's thesis on a fundamental topic. e.g. developing a new general algorithm, but also on a more applied topic, e.g. developing an innovative application. Many students conduct their thesis research as an intern with a company.

Natural Language Processing
Over the past few years, research towards natural language processing has shown strong evidence as to the effectiveness of models that involve both hierarchical structure as well as statistical learning from corpora. In this profile you will study the state-of-the-art statistical models for complex language processing tasks such as parsing, language modeling and machine translation.

A characteristic of some of these models is that they involve defining probability measures over hierarchical structure, e.g., trees and graphs. The profile covers supervised as well as unsupervised methods for learning these models directly from large training corpora and provides the necessary background for research in Computational Linguistics and Natural Language Processing.

Data Science
This specialization focuses on understanding, analyzing and working with large amounts of data. Students study the entire Data Science lifecycle from data acquisition and management to analysis and visualization. These techniques include machine learning and data mining, large scale data management, information visualization and reasoning over web data.

There is a strong emphasis on applying artificial intelligence techniques to Data Science problems and in particular setting up experiments and performing informative analyses. Students will have the opportunity to apply their knowledge to large real world datasets like those from social media or the web. During the final Masters project, students will put together all facets of their education to tackle a data science problem.
AI and the Web
Since its invention in the early '90s, the Web has become the largest information space that has ever been constructed. The Web is not only a very large environment, but it is also very diverse, it combines text, images, video and data, it is very dynamic, noisy, and very, very large. That makes the Web a natural "habitat" for intelligent systems, and many typical AI problems can be investigated in this habitat: Can we build computers that can reason about the information in websites? Can we build search engines that really understand our question, and that give us intelligent answers? Can we build smart agents that travel across the web to collect personalised information? Can we use natural language processing to build computers that can read and understand web pages? Can we use machine learning techniques to automatically categorise webpages, or even to learn which webpages are trustworthy, and which ones are not? The interdisciplinary field of "AI on the Web" combines techniques from such diverse subfields as machine learning, natural language processing, knowledge representation and intelligent agents to tackle these challenging problems.

All the Master’s courses are taught by researchers who are experts in their domain. This ensures you an advanced academic level of education, and integration of the latest developments in the field. The majority of lecturers are also involved in collaborative projects with industry players, creating a link to applications in real-world situations. And the active role our lecturers at international conferences contributes to solid and state-of-the-art course material.

Students

The Master's programme in Artificial Intelligence will have a student population of approximately 75 students each year, with many nationalities and backgrounds. Courses take place in small groups which leads to an informal teaching environment. As a graduate student you are encouraged to regularly present and discuss your work, to optimally learn from the staff and your fellow students.

Pioneer in developing intelligent systems

This programme is a pioneer in the development of intelligent systems. As a Master's student, you will be given the opportunity to work on advanced information systems at a wide range of companies and institutions. Some recent examples include:

Semantic navigation on overheid.nl (the main Dutch government website)

A personal 'quit assistant' to help people give up smoking

In cooperation with Philips, adaptive personal music choices during sports training

New forms of online publication for Elsevier

A knowledge system to predict problems with Amsterdam's trams and other public transport

An intelligent opponent that is able to antipate on player's actions in a real time action game

Research institutes

The joint Master's programme in Artificial Intelligence is strongly connected to research topics of the informatics research institutes of both universities.
The Network Institute brings together researchers from many different academic disciplines, including information systems, communication science, computer science, business and management research, knowledge management, marketing and strategy, economics, artificial intelligence, mathematics, and organization science. The Network Institute is part of VU Amsterdam.

CAMeRA provides an environment for the study and the development of media applications, with the focus on their impact on people's physical and mental wellbeing. CAMeRA recognizes its mission in both fundamental research and applied projects to be socially responsible in nature.

The mission of the Informatics Institute is to perform curiosity driven and use-inspired fundamental research in Computer Science. The research in the institute involves complex information systems at large, with a focus on Collaborative, Data Driven, Computational and Intelligent Systems, all with a strong interactive component.

What are the mathematical properties of information? How can we describe how information flows between humans or computers? Questions such as these lie at the heart of the research conducted at the Institute for Logic, Language and Computation (ILLC), a world-class research institute in the interdisciplinary area between mathematics, linguistics, computer science, philosophy and artificial intelligence.

Arjon Buikstra

Student

"After studying a few months in California, I decided to do my master's project abroad as well. Eventually I spent three months living in Berlin, working at the Max Planck Institute for Human Development."

Overview Artificial Intelligence: Cognitive Science

15 July for Dutch students. 1 April for EU/EEA and non-EU/EEA students.* * EU/EEA students with an international degree who do not need housing services through VU Amsterdam can still apply until 1 June.

START DATE

1 September

STUDY TYPE

Full-time

FIELD OF INTEREST

Behavioural and Social SciencesHealth and MovementComputer Science, Mathematics and BusinessLanguage and Communication

Rianne van Lambalgen

Graduate

"After finishing my studies in Cognitive Psychology I started the specialization Cognitive Science, part of the Master’s programme in Artificial Intelligence. I enjoyed this programme very much as it was a good combination of technically and theoretically challenging material. I learned about theories in psychology, but also their application within artificial intelligence.

For me, this course emphasized the practical use of scientific research, which is one of the reasons I started my current PhD position at the Agent Research group at the department of Artificial Intelligence.

In addition to the interesting content, joining this Master’s programme was also fun as the group is relatively small and practical work is often done in small groups, which gives you a good opportunity to meet people."

Dutch students

For the Master programmes in Artificial Intelligence, students may enrol who have a Bachelor or Drs diploma in Artificial Intelligence obtained at a Dutch university (Utrecht, Nijmegen, Amsterdam (VU, UvA), Groningen, Maastricht). Some of the programmes are open to students who have a Bachelor, Drs or Master diploma in Computer Science, Psychology, Biology, or Law obtained at a Dutch institute or university of quality recognized by VU Amsterdam. Some of the programmes are also open to people with other diplomas, University or HBO, who are kindly invited to contact us if interested in following a Master programme at our Department.

International students

Admission to this Master programme is open to students with a Bachelor degree in Artificial Intelligence or students from Computer Science with appropriate specialization. The student is assumed to be familiar with programming in Java and Prolog, to have a general knowledge of Artificial Intelligence and a basic working knowledge of computer science, logic, mathematics, psychology and natural language processing. Under specific circumstances other students may also be admissible.